Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+# Load model and tokenizer
+model_name = "nateraw/bert-base-uncased-emotion"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Emotion labels
+labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
+
+# Prediction function
+def predict_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+    pred_class = torch.argmax(probs).item()
+    emotion = labels[pred_class]
+    return f"{emotion} ({probs[0][pred_class].item()*100:.2f}% confidence)"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=2, placeholder="Type something here..."),
+    outputs="text",
+    title="BERT-based Emotion Detection",
+    description="A web app that uses a fine-tuned BERT model to detect emotions from text."
+)
+
+interface.launch()